Computing probability density function at a point, given the covariance matrix and mean

(Edited for clarity.)

Say I have the variance-covariance matrix $$\mathbf{V}$$ and mean $$\mathbf{\mu}$$ of a multivariate normal distribution. Given a sample, $$\mathbf{s}$$, can I compute/estimate the value of the probability density function at that sample?

I'm trying to determine how well a given sample "fits" the various distributions determined by some $$\mathbf{V}_i$$ and $$\mathbf{\mu}_i$$ in order to label the sample with $$\underset{i \in 0, 1, ...}{\arg\max\,}{f(\mathbf{s}; \mathbf{V}_i, \mathbf{\mu}_i)}$$.

• For anyone else who comes with the same question: yes, there is an analytic form for the PDF of the multivariate normal, and it's fairly convenient; see, for example, the scipy docs here – Linuxios Jul 12 at 19:18

$$\ell_\mathbf{s}(\mathbf{V}) = - \frac{1}{2} \text{det}(\mathbf{V}) -\frac{1}{2} \mathbf{s}^\text{T} \mathbf{V}^{-1} \mathbf{s} \quad \quad \quad \text{for all } \mathbf{V} \in \mathcal{V},$$
where $$\mathcal{V}$$ is the set of real square positive-definite matrices of the required dimension.
• In practice, I'm using the maximum-likelihood empirical covariance estimator to get $\mathrm{V}$ in the first place, so making that same assumption (multivariate Gaussian) here is no worse than making it in the first place, right? – Linuxios Jul 11 at 0:28
• Further, I think I'm looking for a $\ell_\mathbf{V}(\mathbf{s})$, for lack of a better way to put it. Does that have the same form? The likelihood of the sample $\mathbf{s}$ given the mean, covariance matrix, and the assumption of multivariate normal distribution. – Linuxios Jul 11 at 0:38
• Okay, well in that case you can use the formula above. (Note that I have omitted the constant term $-\tfrac{n}{2} \cdot \ln (2 \pi)$ from the function, where $k$ is the length of the vector $\mathbf{s}$. The computer program you are using would probably include this term.) – Ben Jul 11 at 1:01